Is Allennlp Safe?
Allennlp — Nerq Trust Score 74.8/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Allennlp is a Python package with a Nerq Trust Score of 74.8/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Allennlp safe?
Trust Score Breakdown — Allennlp has a Nerq Trust Score of 74.8/100 (B). Measured across 2 independent trust signals (as of 2026-03-20).
What is Allennlp's trust score?
Allennlp has a Nerq Trust Score of 74.8/100, earning a B grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Allennlp?
Allennlp's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Allennlp and who maintains it?
| Author | Allen Institute for Artificial Intelligence |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Allennlp
What is Allennlp?
Allennlp is a Python package — An open-source NLP research library, built on PyTorch..
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=allennlp
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Allennlp has a Nerq Trust Score of 75/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Allennlp has a Trust Score of 75/100 (B).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.